Gaussian processes [1]}
are a non-parametric distance aware output function.
GPs model the similarity between data points
with a kernel function, and use the Bayes rule to model a distribution over functions by maximizing the marginal likelihood [1]}. As such, GPs require access to the full datasest at inference time, and although there exsit approximations, this family of methods does not scale well with the dimension of the data.
